{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import os\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_path = r\"D:/Code/Kaggle/2_HousePrices/input/train.csv\"\n",
    "test_path = r\"D:/Code/Kaggle/2_HousePrices/input/test.csv\"\n",
    "train_dataset = pd.read_csv(train_path)\n",
    "test_dataset = pd.read_csv(test_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>...</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>208500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>181500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>223500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1455</th>\n",
       "      <td>1456</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>62.0</td>\n",
       "      <td>7917</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>175000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1456</th>\n",
       "      <td>1457</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>85.0</td>\n",
       "      <td>13175</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>210000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1457</th>\n",
       "      <td>1458</td>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>66.0</td>\n",
       "      <td>9042</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GdPrv</td>\n",
       "      <td>Shed</td>\n",
       "      <td>2500</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>266500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1458</th>\n",
       "      <td>1459</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>68.0</td>\n",
       "      <td>9717</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>142125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1459</th>\n",
       "      <td>1460</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>75.0</td>\n",
       "      <td>9937</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>147500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1460 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "0        1          60       RL         65.0     8450   Pave   NaN      Reg   \n",
       "1        2          20       RL         80.0     9600   Pave   NaN      Reg   \n",
       "2        3          60       RL         68.0    11250   Pave   NaN      IR1   \n",
       "3        4          70       RL         60.0     9550   Pave   NaN      IR1   \n",
       "4        5          60       RL         84.0    14260   Pave   NaN      IR1   \n",
       "...    ...         ...      ...          ...      ...    ...   ...      ...   \n",
       "1455  1456          60       RL         62.0     7917   Pave   NaN      Reg   \n",
       "1456  1457          20       RL         85.0    13175   Pave   NaN      Reg   \n",
       "1457  1458          70       RL         66.0     9042   Pave   NaN      Reg   \n",
       "1458  1459          20       RL         68.0     9717   Pave   NaN      Reg   \n",
       "1459  1460          20       RL         75.0     9937   Pave   NaN      Reg   \n",
       "\n",
       "     LandContour Utilities  ... PoolArea PoolQC  Fence MiscFeature MiscVal  \\\n",
       "0            Lvl    AllPub  ...        0    NaN    NaN         NaN       0   \n",
       "1            Lvl    AllPub  ...        0    NaN    NaN         NaN       0   \n",
       "2            Lvl    AllPub  ...        0    NaN    NaN         NaN       0   \n",
       "3            Lvl    AllPub  ...        0    NaN    NaN         NaN       0   \n",
       "4            Lvl    AllPub  ...        0    NaN    NaN         NaN       0   \n",
       "...          ...       ...  ...      ...    ...    ...         ...     ...   \n",
       "1455         Lvl    AllPub  ...        0    NaN    NaN         NaN       0   \n",
       "1456         Lvl    AllPub  ...        0    NaN  MnPrv         NaN       0   \n",
       "1457         Lvl    AllPub  ...        0    NaN  GdPrv        Shed    2500   \n",
       "1458         Lvl    AllPub  ...        0    NaN    NaN         NaN       0   \n",
       "1459         Lvl    AllPub  ...        0    NaN    NaN         NaN       0   \n",
       "\n",
       "     MoSold YrSold  SaleType  SaleCondition  SalePrice  \n",
       "0         2   2008        WD         Normal     208500  \n",
       "1         5   2007        WD         Normal     181500  \n",
       "2         9   2008        WD         Normal     223500  \n",
       "3         2   2006        WD        Abnorml     140000  \n",
       "4        12   2008        WD         Normal     250000  \n",
       "...     ...    ...       ...            ...        ...  \n",
       "1455      8   2007        WD         Normal     175000  \n",
       "1456      2   2010        WD         Normal     210000  \n",
       "1457      5   2010        WD         Normal     266500  \n",
       "1458      4   2010        WD         Normal     142125  \n",
       "1459      6   2008        WD         Normal     147500  \n",
       "\n",
       "[1460 rows x 81 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1460 entries, 0 to 1459\n",
      "Data columns (total 81 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Id             1460 non-null   int64  \n",
      " 1   MSSubClass     1460 non-null   int64  \n",
      " 2   MSZoning       1460 non-null   object \n",
      " 3   LotFrontage    1201 non-null   float64\n",
      " 4   LotArea        1460 non-null   int64  \n",
      " 5   Street         1460 non-null   object \n",
      " 6   Alley          91 non-null     object \n",
      " 7   LotShape       1460 non-null   object \n",
      " 8   LandContour    1460 non-null   object \n",
      " 9   Utilities      1460 non-null   object \n",
      " 10  LotConfig      1460 non-null   object \n",
      " 11  LandSlope      1460 non-null   object \n",
      " 12  Neighborhood   1460 non-null   object \n",
      " 13  Condition1     1460 non-null   object \n",
      " 14  Condition2     1460 non-null   object \n",
      " 15  BldgType       1460 non-null   object \n",
      " 16  HouseStyle     1460 non-null   object \n",
      " 17  OverallQual    1460 non-null   int64  \n",
      " 18  OverallCond    1460 non-null   int64  \n",
      " 19  YearBuilt      1460 non-null   int64  \n",
      " 20  YearRemodAdd   1460 non-null   int64  \n",
      " 21  RoofStyle      1460 non-null   object \n",
      " 22  RoofMatl       1460 non-null   object \n",
      " 23  Exterior1st    1460 non-null   object \n",
      " 24  Exterior2nd    1460 non-null   object \n",
      " 25  MasVnrType     1452 non-null   object \n",
      " 26  MasVnrArea     1452 non-null   float64\n",
      " 27  ExterQual      1460 non-null   object \n",
      " 28  ExterCond      1460 non-null   object \n",
      " 29  Foundation     1460 non-null   object \n",
      " 30  BsmtQual       1423 non-null   object \n",
      " 31  BsmtCond       1423 non-null   object \n",
      " 32  BsmtExposure   1422 non-null   object \n",
      " 33  BsmtFinType1   1423 non-null   object \n",
      " 34  BsmtFinSF1     1460 non-null   int64  \n",
      " 35  BsmtFinType2   1422 non-null   object \n",
      " 36  BsmtFinSF2     1460 non-null   int64  \n",
      " 37  BsmtUnfSF      1460 non-null   int64  \n",
      " 38  TotalBsmtSF    1460 non-null   int64  \n",
      " 39  Heating        1460 non-null   object \n",
      " 40  HeatingQC      1460 non-null   object \n",
      " 41  CentralAir     1460 non-null   object \n",
      " 42  Electrical     1459 non-null   object \n",
      " 43  1stFlrSF       1460 non-null   int64  \n",
      " 44  2ndFlrSF       1460 non-null   int64  \n",
      " 45  LowQualFinSF   1460 non-null   int64  \n",
      " 46  GrLivArea      1460 non-null   int64  \n",
      " 47  BsmtFullBath   1460 non-null   int64  \n",
      " 48  BsmtHalfBath   1460 non-null   int64  \n",
      " 49  FullBath       1460 non-null   int64  \n",
      " 50  HalfBath       1460 non-null   int64  \n",
      " 51  BedroomAbvGr   1460 non-null   int64  \n",
      " 52  KitchenAbvGr   1460 non-null   int64  \n",
      " 53  KitchenQual    1460 non-null   object \n",
      " 54  TotRmsAbvGrd   1460 non-null   int64  \n",
      " 55  Functional     1460 non-null   object \n",
      " 56  Fireplaces     1460 non-null   int64  \n",
      " 57  FireplaceQu    770 non-null    object \n",
      " 58  GarageType     1379 non-null   object \n",
      " 59  GarageYrBlt    1379 non-null   float64\n",
      " 60  GarageFinish   1379 non-null   object \n",
      " 61  GarageCars     1460 non-null   int64  \n",
      " 62  GarageArea     1460 non-null   int64  \n",
      " 63  GarageQual     1379 non-null   object \n",
      " 64  GarageCond     1379 non-null   object \n",
      " 65  PavedDrive     1460 non-null   object \n",
      " 66  WoodDeckSF     1460 non-null   int64  \n",
      " 67  OpenPorchSF    1460 non-null   int64  \n",
      " 68  EnclosedPorch  1460 non-null   int64  \n",
      " 69  3SsnPorch      1460 non-null   int64  \n",
      " 70  ScreenPorch    1460 non-null   int64  \n",
      " 71  PoolArea       1460 non-null   int64  \n",
      " 72  PoolQC         7 non-null      object \n",
      " 73  Fence          281 non-null    object \n",
      " 74  MiscFeature    54 non-null     object \n",
      " 75  MiscVal        1460 non-null   int64  \n",
      " 76  MoSold         1460 non-null   int64  \n",
      " 77  YrSold         1460 non-null   int64  \n",
      " 78  SaleType       1460 non-null   object \n",
      " 79  SaleCondition  1460 non-null   object \n",
      " 80  SalePrice      1460 non-null   int64  \n",
      "dtypes: float64(3), int64(35), object(43)\n",
      "memory usage: 924.0+ KB\n"
     ]
    }
   ],
   "source": [
    "train_dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      1460.000000\n",
       "mean     180921.195890\n",
       "std       79442.502883\n",
       "min       34900.000000\n",
       "25%      129975.000000\n",
       "50%      163000.000000\n",
       "75%      214000.000000\n",
       "max      755000.000000\n",
       "Name: SalePrice, dtype: float64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_dataset.get(\"SalePrice\").describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x1152 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(16, 16))\n",
    "sns.distplot(train_dataset.get(\"SalePrice\"), kde=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x1152 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corrmat = train_dataset.corr()\n",
    "f, ax = plt.subplots(figsize=(16, 16))\n",
    "sns.heatmap(corrmat, vmax=.8, square=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x1152 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,16))\n",
    "columns = corrmat.nlargest(10, 'SalePrice')['SalePrice'].index\n",
    "correlation_matrix = np.corrcoef(train_dataset[columns].values.T)\n",
    "sns.set(font_scale=1.25)\n",
    "heat_map = sns.heatmap(correlation_matrix, cbar=True, annot=True, square=True, fmt='.2f', annot_kws={'size': 10}, yticklabels=columns.values, xticklabels=columns.values)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PoolQC</th>\n",
       "      <td>1453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MiscFeature</th>\n",
       "      <td>1406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alley</th>\n",
       "      <td>1369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fence</th>\n",
       "      <td>1179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FireplaceQu</th>\n",
       "      <td>690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LotFrontage</th>\n",
       "      <td>259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageYrBlt</th>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageCond</th>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageType</th>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageFinish</th>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageQual</th>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFinType2</th>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtExposure</th>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtQual</th>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtCond</th>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFinType1</th>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MasVnrArea</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MasVnrType</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Electrical</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Id</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Functional</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fireplaces</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KitchenQual</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KitchenAbvGr</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BedroomAbvGr</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HalfBath</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FullBath</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtHalfBath</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TotRmsAbvGrd</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageCars</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Total\n",
       "PoolQC         1453\n",
       "MiscFeature    1406\n",
       "Alley          1369\n",
       "Fence          1179\n",
       "FireplaceQu     690\n",
       "LotFrontage     259\n",
       "GarageYrBlt      81\n",
       "GarageCond       81\n",
       "GarageType       81\n",
       "GarageFinish     81\n",
       "GarageQual       81\n",
       "BsmtFinType2     38\n",
       "BsmtExposure     38\n",
       "BsmtQual         37\n",
       "BsmtCond         37\n",
       "BsmtFinType1     37\n",
       "MasVnrArea        8\n",
       "MasVnrType        8\n",
       "Electrical        1\n",
       "Id                0\n",
       "Functional        0\n",
       "Fireplaces        0\n",
       "KitchenQual       0\n",
       "KitchenAbvGr      0\n",
       "BedroomAbvGr      0\n",
       "HalfBath          0\n",
       "FullBath          0\n",
       "BsmtHalfBath      0\n",
       "TotRmsAbvGrd      0\n",
       "GarageCars        0"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total = train_dataset.isna().sum().sort_values(ascending=False)\n",
    "\n",
    "missing_data = pd.concat([total], axis=1, keys=[\"Total\"])\n",
    "\n",
    "missing_data.head(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataset = train_dataset.drop((missing_data[missing_data.get(\"Total\") > 1]).index, 1)\n",
    "train_dataset = train_dataset.drop(train_dataset.loc[train_dataset.get(\"Electrical\").isna()].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_dataset.isna().sum().max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1459, 63)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "categories = list(train_dataset.select_dtypes([\"object\"]))\n",
    "ct = ColumnTransformer(transformers=[('encoder', OneHotEncoder(), categories)], remainder='passthrough')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>LotConfig</th>\n",
       "      <th>LandSlope</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>...</th>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <th>3SsnPorch</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>8450</td>\n",
       "      <td>Pave</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>CollgCr</td>\n",
       "      <td>...</td>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>9600</td>\n",
       "      <td>Pave</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>FR2</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>Veenker</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>11250</td>\n",
       "      <td>Pave</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>CollgCr</td>\n",
       "      <td>...</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>9550</td>\n",
       "      <td>Pave</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Corner</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>Crawfor</td>\n",
       "      <td>...</td>\n",
       "      <td>35</td>\n",
       "      <td>272</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>14260</td>\n",
       "      <td>Pave</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>FR2</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>NoRidge</td>\n",
       "      <td>...</td>\n",
       "      <td>84</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1455</th>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>7917</td>\n",
       "      <td>Pave</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>Gilbert</td>\n",
       "      <td>...</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1456</th>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>13175</td>\n",
       "      <td>Pave</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>NWAmes</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1457</th>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>9042</td>\n",
       "      <td>Pave</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>Crawfor</td>\n",
       "      <td>...</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2500</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1458</th>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>9717</td>\n",
       "      <td>Pave</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>NAmes</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>112</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1459</th>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>9937</td>\n",
       "      <td>Pave</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>Edwards</td>\n",
       "      <td>...</td>\n",
       "      <td>68</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1459 rows × 61 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      MSSubClass MSZoning  LotArea Street LotShape LandContour Utilities  \\\n",
       "0             60       RL     8450   Pave      Reg         Lvl    AllPub   \n",
       "1             20       RL     9600   Pave      Reg         Lvl    AllPub   \n",
       "2             60       RL    11250   Pave      IR1         Lvl    AllPub   \n",
       "3             70       RL     9550   Pave      IR1         Lvl    AllPub   \n",
       "4             60       RL    14260   Pave      IR1         Lvl    AllPub   \n",
       "...          ...      ...      ...    ...      ...         ...       ...   \n",
       "1455          60       RL     7917   Pave      Reg         Lvl    AllPub   \n",
       "1456          20       RL    13175   Pave      Reg         Lvl    AllPub   \n",
       "1457          70       RL     9042   Pave      Reg         Lvl    AllPub   \n",
       "1458          20       RL     9717   Pave      Reg         Lvl    AllPub   \n",
       "1459          20       RL     9937   Pave      Reg         Lvl    AllPub   \n",
       "\n",
       "     LotConfig LandSlope Neighborhood  ... OpenPorchSF EnclosedPorch  \\\n",
       "0       Inside       Gtl      CollgCr  ...          61             0   \n",
       "1          FR2       Gtl      Veenker  ...           0             0   \n",
       "2       Inside       Gtl      CollgCr  ...          42             0   \n",
       "3       Corner       Gtl      Crawfor  ...          35           272   \n",
       "4          FR2       Gtl      NoRidge  ...          84             0   \n",
       "...        ...       ...          ...  ...         ...           ...   \n",
       "1455    Inside       Gtl      Gilbert  ...          40             0   \n",
       "1456    Inside       Gtl       NWAmes  ...           0             0   \n",
       "1457    Inside       Gtl      Crawfor  ...          60             0   \n",
       "1458    Inside       Gtl        NAmes  ...           0           112   \n",
       "1459    Inside       Gtl      Edwards  ...          68             0   \n",
       "\n",
       "     3SsnPorch ScreenPorch  PoolArea  MiscVal  MoSold  YrSold SaleType  \\\n",
       "0            0           0         0        0       2    2008       WD   \n",
       "1            0           0         0        0       5    2007       WD   \n",
       "2            0           0         0        0       9    2008       WD   \n",
       "3            0           0         0        0       2    2006       WD   \n",
       "4            0           0         0        0      12    2008       WD   \n",
       "...        ...         ...       ...      ...     ...     ...      ...   \n",
       "1455         0           0         0        0       8    2007       WD   \n",
       "1456         0           0         0        0       2    2010       WD   \n",
       "1457         0           0         0     2500       5    2010       WD   \n",
       "1458         0           0         0        0       4    2010       WD   \n",
       "1459         0           0         0        0       6    2008       WD   \n",
       "\n",
       "     SaleCondition  \n",
       "0           Normal  \n",
       "1           Normal  \n",
       "2           Normal  \n",
       "3          Abnorml  \n",
       "4           Normal  \n",
       "...            ...  \n",
       "1455        Normal  \n",
       "1456        Normal  \n",
       "1457        Normal  \n",
       "1458        Normal  \n",
       "1459        Normal  \n",
       "\n",
       "[1459 rows x 61 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = train_dataset.drop(['Id', 'SalePrice'], axis=1)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1459, 61)\n"
     ]
    }
   ],
   "source": [
    "print(X.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1459, 62)\n"
     ]
    }
   ],
   "source": [
    "test_dataset = test_dataset.drop((missing_data[missing_data.get(\"Total\") > 1]).index, 1)\n",
    "print(test_dataset.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1459, 220)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = ct.fit_transform(X)\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1459 entries, 0 to 1458\n",
      "Data columns (total 62 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Id             1459 non-null   int64  \n",
      " 1   MSSubClass     1459 non-null   int64  \n",
      " 2   MSZoning       1455 non-null   object \n",
      " 3   LotArea        1459 non-null   int64  \n",
      " 4   Street         1459 non-null   object \n",
      " 5   LotShape       1459 non-null   object \n",
      " 6   LandContour    1459 non-null   object \n",
      " 7   Utilities      1457 non-null   object \n",
      " 8   LotConfig      1459 non-null   object \n",
      " 9   LandSlope      1459 non-null   object \n",
      " 10  Neighborhood   1459 non-null   object \n",
      " 11  Condition1     1459 non-null   object \n",
      " 12  Condition2     1459 non-null   object \n",
      " 13  BldgType       1459 non-null   object \n",
      " 14  HouseStyle     1459 non-null   object \n",
      " 15  OverallQual    1459 non-null   int64  \n",
      " 16  OverallCond    1459 non-null   int64  \n",
      " 17  YearBuilt      1459 non-null   int64  \n",
      " 18  YearRemodAdd   1459 non-null   int64  \n",
      " 19  RoofStyle      1459 non-null   object \n",
      " 20  RoofMatl       1459 non-null   object \n",
      " 21  Exterior1st    1458 non-null   object \n",
      " 22  Exterior2nd    1458 non-null   object \n",
      " 23  ExterQual      1459 non-null   object \n",
      " 24  ExterCond      1459 non-null   object \n",
      " 25  Foundation     1459 non-null   object \n",
      " 26  BsmtFinSF1     1458 non-null   float64\n",
      " 27  BsmtFinSF2     1458 non-null   float64\n",
      " 28  BsmtUnfSF      1458 non-null   float64\n",
      " 29  TotalBsmtSF    1458 non-null   float64\n",
      " 30  Heating        1459 non-null   object \n",
      " 31  HeatingQC      1459 non-null   object \n",
      " 32  CentralAir     1459 non-null   object \n",
      " 33  Electrical     1459 non-null   object \n",
      " 34  1stFlrSF       1459 non-null   int64  \n",
      " 35  2ndFlrSF       1459 non-null   int64  \n",
      " 36  LowQualFinSF   1459 non-null   int64  \n",
      " 37  GrLivArea      1459 non-null   int64  \n",
      " 38  BsmtFullBath   1457 non-null   float64\n",
      " 39  BsmtHalfBath   1457 non-null   float64\n",
      " 40  FullBath       1459 non-null   int64  \n",
      " 41  HalfBath       1459 non-null   int64  \n",
      " 42  BedroomAbvGr   1459 non-null   int64  \n",
      " 43  KitchenAbvGr   1459 non-null   int64  \n",
      " 44  KitchenQual    1458 non-null   object \n",
      " 45  TotRmsAbvGrd   1459 non-null   int64  \n",
      " 46  Functional     1457 non-null   object \n",
      " 47  Fireplaces     1459 non-null   int64  \n",
      " 48  GarageCars     1458 non-null   float64\n",
      " 49  GarageArea     1458 non-null   float64\n",
      " 50  PavedDrive     1459 non-null   object \n",
      " 51  WoodDeckSF     1459 non-null   int64  \n",
      " 52  OpenPorchSF    1459 non-null   int64  \n",
      " 53  EnclosedPorch  1459 non-null   int64  \n",
      " 54  3SsnPorch      1459 non-null   int64  \n",
      " 55  ScreenPorch    1459 non-null   int64  \n",
      " 56  PoolArea       1459 non-null   int64  \n",
      " 57  MiscVal        1459 non-null   int64  \n",
      " 58  MoSold         1459 non-null   int64  \n",
      " 59  YrSold         1459 non-null   int64  \n",
      " 60  SaleType       1458 non-null   object \n",
      " 61  SaleCondition  1459 non-null   object \n",
      "dtypes: float64(8), int64(26), object(28)\n",
      "memory usage: 706.8+ KB\n"
     ]
    }
   ],
   "source": [
    "test_dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = test_dataset.drop([\"Id\"], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1459, 61)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i in X_test.isna().columns:\n",
    "    if X_test.dtypes[i] != \"object\":\n",
    "        X_test[i] = X_test[i].fillna(X_test[i].mean())\n",
    "    else:\n",
    "        X_test[i] = X_test[i].fillna(X_test[i]. mode()[0])\n",
    "\n",
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.isna().sum().max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = ct.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1459, 220)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = train_dataset.SalePrice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_val, y_train, y_val = train_test_split(X, y, train_size=0.8, random_state=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "LinearRegression model 线性回归模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "lr_regressor = LinearRegression()\n",
    "lr_regressor.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = lr_regressor.predict(X_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8839482172387989"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_regressor.score(X_val, y_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.metrics import mean_squared_error\n",
    "# print(f\"Mean square error: {mean_squared_error(np.log2(y_val), np.log2(y_pred))}\")\n",
    "# print(f\"Root mean square error: {mean_squared_error(np.log2(y_val), np.log2(y_pred), squared=False)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_preds =lr_regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1459,)"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_preds.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1459,)"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_dataset.Id.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "# output = pd.DataFrame({'Id':test_dataset.Id, 'SalePrice':test_preds})\n",
    "# output.to_csv('submission.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "DecisionTreeRegressor model 决策树模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(max_depth=10, random_state=142)"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "dt_regressor = DecisionTreeRegressor(max_depth=10, random_state=142)\n",
    "dt_regressor.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_preds = dt_regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6769799942604471"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt_regressor.score(X_val, y_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "# output = pd.DataFrame({'Id': test_dataset.Id, 'SalePrice': y_preds})\n",
    "# output.to_csv('submission.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Random Forest Regression 随机森林"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(max_depth=15, random_state=42)"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "rf_regressor = RandomForestRegressor(max_depth=15, n_estimators=100, random_state=42)\n",
    "rf_regressor.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_preds = rf_regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "# output = pd.DataFrame({'Id': test_dataset.Id, 'SalePrice': y_preds})\n",
    "# output.to_csv('submission.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Gradient Boosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingRegressor(learning_rate=1.0, max_depth=15)"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "\n",
    "gb_regressor = GradientBoostingRegressor(max_depth=15, n_estimators=100, learning_rate=1.0)\n",
    "gb_regressor.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_preds = gb_regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "# output = pd.DataFrame({'Id': test_dataset.Id, 'SalePrice': y_preds})\n",
    "# output.to_csv('submission.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "             colsample_bynode=1, colsample_bytree=1, enable_categorical=False,\n",
       "             gamma=0, gpu_id=-1, importance_type=None,\n",
       "             interaction_constraints='', learning_rate=0.300000012,\n",
       "             max_delta_step=0, max_depth=6, min_child_weight=1, missing=nan,\n",
       "             monotone_constraints='()', n_estimators=100, n_jobs=4,\n",
       "             num_parallel_tree=1, predictor='auto', random_state=0, reg_alpha=0,\n",
       "             reg_lambda=1, scale_pos_weight=1, subsample=1, tree_method='exact',\n",
       "             validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import xgboost\n",
    "\n",
    "xgb_regressor = xgboost.XGBRegressor()\n",
    "xgb_regressor.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_preds = xgb_regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "# output = pd.DataFrame({'Id': test_dataset.Id, 'SalePrice': y_preds})\n",
    "# output.to_csv('submission.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ridge Regression 岭回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ridge(alpha=1)"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import Ridge\n",
    "\n",
    "ridge_regressor = Ridge(alpha=1, solver=\"auto\")\n",
    "ridge_regressor.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8226588479234758"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridge_regressor.score(X_val, y_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_preds = ridge_regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "# output = pd.DataFrame({'Id': test_dataset.Id, 'SalePrice': y_preds})\n",
    "# output.to_csv('submission.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lasso Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Lasso\n",
    "\n",
    "alpha_values = np.arange(0, 1, 0.1).tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score and alpha value: 0.8883997674865276-----0.0\n",
      "Score and alpha value: 0.8836143012384587-----0.1\n",
      "Score and alpha value: 0.8837387876809888-----0.2\n",
      "Score and alpha value: 0.883908859444259-----0.30000000000000004\n",
      "Score and alpha value: 0.8840863213146422-----0.4\n",
      "Score and alpha value: 0.8842520203472876-----0.5\n",
      "Score and alpha value: 0.884408744920294-----0.6000000000000001\n",
      "Score and alpha value: 0.8845606419983109-----0.7000000000000001\n",
      "Score and alpha value: 0.8847088926525203-----0.8\n",
      "Score and alpha value: 0.8848512353672766-----0.9\n",
      "0.8883997674865276 0.0\n"
     ]
    }
   ],
   "source": [
    "max_score = 0\n",
    "best_alpha_value = 0\n",
    "for i in alpha_values:\n",
    "    lasso_regressor = Lasso(alpha=i)\n",
    "    lasso_regressor.fit(X_train, y_train)\n",
    "    current_score = lasso_regressor.score(X_val, y_val)\n",
    "    print(f\"Score and alpha value: {current_score}-----{i}\")\n",
    "    if(current_score > max_score):\n",
    "        max_score = current_score\n",
    "        best_alpha_value = i\n",
    "print(max_score, best_alpha_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8883997674865276"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lasso_regressor = Lasso(alpha=0)\n",
    "lasso_regressor.fit(X_train, y_train)\n",
    "lasso_regressor.score(X_val, y_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_preds = lasso_regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "# output = pd.DataFrame({'Id': test_dataset.Id, 'SalePrice': y_preds})\n",
    "# output.to_csv('submission.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ElasticNet Regressor 弹性网络回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8962763242495179"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import ElasticNet\n",
    "\n",
    "elastic_net_regressor = ElasticNet(alpha=0.1, l1_ratio=0.5)\n",
    "elastic_net_regressor.fit(X_train, y_train)\n",
    "elastic_net_regressor.score(X_val, y_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_preds = elastic_net_regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "# output = pd.DataFrame({'Id': test_dataset.Id, 'SalePrice': y_preds})\n",
    "# output.to_csv('submission.csv', index=False)"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "a2c34d9a4665a32586132f6075dae38c10849468581126e5dc73edddb31ce81a"
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  "kernelspec": {
   "display_name": "Python 3.10.1 ('houseprices': venv)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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